Congratulations to the entire Event Horizon Telescope team, and especially to Dr. Katie Bouman who is joining the Engineering and Applied Science (EAS) Division in June as assistant professor of computing and mathematical sciences (CMS). Currently, Caltech and CO Architects are working with her to design and construct a unique laboratory that will facilitate her work in computational imaging. The laboratory is the first of its kind and is designed for her to conduct experimental work in conjunction with her computational approaches – making it possible, for instance, to observe phenomena previously difficult or impossible to measure. The black hole imaging is one spectacular example of how Professor Bouman’s algorithms are advancing our knowledge of the world; she has also developed algorithms that let us “see around corners” and detect material properties (such as stiffness and dampness) via imaging. In her work, Bouman has also developed methods to combine information from both imaging as well as acoustic systems to analyze sub-pixel scale vibrations of otherwise seemingly still objects. As a result, relatively inexpensive cameras, combined with powerful algorithms, are an increasingly attractive alternative to complex and expensive laser-based systems to sense “invisible” attributes of a material. [Caltech story - How to Take a Picture of a Black Hole]
Lihong Wang, Bren Professor of Medical Engineering and Electrical Engineering, and colleagues are using photoacoustic microscopy (PAM) to improve on an existing technology for measuring the oxygen-consumption rate (OCR). This new method allows the researchers to determine how oxygenated a sample of blood is by "listening" to the sound it makes when illuminated by the laser. Professor Wang calls this single-cell metabolic photoacoustic microscopy, or SCM-PAM. [Caltech story]
Erik Winfree, Professor of Computer Science, Computation and Neural Systems, and Bioengineering, and colleagues have designed DNA molecules that can carry out reprogrammable computations, for the first time creating so-called algorithmic self-assembly in which the same "hardware" can be configured to run different "software." Although DNA computers have the potential to perform more complex computations than the ones featured in the Nature paper, Professor Winfree cautions that one should not expect them to start replacing the standard silicon microchip computers. That is not the point of this research. "These are rudimentary computations, but they have the power to teach us more about how simple molecular processes like self-assembly can encode information and carry out algorithms. Biology is proof that chemistry is inherently information-based and can store information that can direct algorithmic behavior at the molecular level," he says. [Caltech story]
Ognjen Ilic, postdoctoral scholar in Professor Harry Atwater’s laboratory, and colleagues have designed a way to levitate and propel objects using only light, by creating specific nanoscale patterning on the objects' surfaces. "We have come up with a method that could levitate macroscopic objects," says Professor Atwater, who is also the director of the Joint Center for Artificial Photosynthesis. "There is an audaciously interesting application to use this technique as a means for propulsion of a new generation of spacecraft. We're a long way from actually doing that, but we are in the process of testing out the principles." [Caltech story]
Julia Greer, Professor of Materials Science, Mechanics and Medical Engineering, and colleagues have determined that the failure of architected materials—the point at which they break when compressed or stretched—can be described using classical continuum mechanics, which models the behavior of a material as a continuous mass rather than as individual (or "discrete") particles. This finding implies a duality to the nature of these materials—in that they can be thought of both as individual particles and also as a single collective. [Caltech story]
Nadia Lapusta, Professor of Mechanical Engineering and Geophysics, creates computer models of earthquakes by integrating an astonishing range of data—on scales from thousands of kilometers down to microns and from millennia down to thousandths of a second. “You have to understand the mechanics across the entire earthquake system, starting at the micrometer scale,” says Professor Lapusta. “This is the challenge.” Her numerical models rely upon field observations, seismic monitoring, lab experiments, and theoretical science, while complementing those endeavors with a new perspective. The predictions expand researchers’ view beyond the limits of direct observation—which is important for events that occur across thousands of years. [Breakthrough story] [ENGenious story]
Professor Yisong Yue is collaborating with Caltech seismologists to use artificial intelligence (AI) to improve the automated processes that identify earthquake waves and assess the strength, speed, and direction of shaking in real time. Professor Yue explains, “the reasons why AI can be a good tool have to do with scale and complexity coupled with an abundant amount of data. Earthquake monitoring systems generate massive data sets that need to be processed in order to provide useful information to scientists. AI can do that faster and more accurately than humans can, and even find patterns that would otherwise escape the human eye.” [Read the full Q&A]
"Projections with current climate models—for example, of how features such as rainfall extremes will change—still have large uncertainties, and the uncertainties are poorly quantified," says Professor Tapio Schneider, principal investigator of the Climate Modeling Alliance (CliMA). "For cities planning their stormwater management infrastructure to withstand the next 100 years' worth of floods, this is a serious issue; concrete answers about the likely range of climate outcomes are key for planning." The new climate model will be built by a consortium of researchers led by Caltech, in partnership with MIT; the Naval Postgraduate School (NPS); and JPL, which Caltech manages for NASA. It will use data-assimilation and machine-learning tools to improve itself in real time, harnessing both Earth observations and the nested high-resolution simulations. "The success of computational weather forecasting demonstrates the power of using data to improve the accuracy of computer models; we aim to bring the same successes to climate prediction," says Professor Andrew Stuart. [Caltech story]
Chiara Daraio, Professor of Mechanical Engineering and Applied Physics, and colleagues have developed phononic devices that include parts that vibrate extremely fast, moving back and forth up to tens of millions of times per second. The devices were developed by creating silicon nitride drums that are just 90 nanometers thick. The drums are arranged into grids, with different grid patterns having different properties. Professor Daraio, along with former Caltech postdoctoral scholar Jinwoong Cha, have shown that arrays of these drums can act as tunable filters for signals of different frequencies and can act like one-way valves for high-frequency waves. [Caltech story]
Thanks to Professor Pietro Perona and his graduate students including Grant Van Horn and Sara Beery, the next wildlife photo you snap might set you on a path to helping map life on Earth. “The whole web, this huge repository of wonderful information, is indexed by words,” Perona says. “But when we have an image—a visual query—we don’t know what to do unless there is an expert next to us. We’ve gotten so numb to the idea that we’ll never find the answer out.” [Breakthrough story]
